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<table width="100%" summary="page for foodstamp"><tr><td>foodstamp</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Food Stamp Program Participation</h2>

<h3>Description</h3>

<p>This data consists of 150 randomly selected persons from a survey
with information on over 2000 elderly US citizens, where the response,
indicates participation in the U.S. Food Stamp Program.
</p>


<h3>Usage</h3>

<pre>data(foodstamp, package="robustbase")</pre>


<h3>Format</h3>

<p>A data frame with 150 observations on the following 4 variables.
</p>

<dl>
<dt><code>participation</code></dt><dd><p>participation in U.S. Food Stamp Program; yes = 1, no = 0</p>
</dd>
<dt><code>tenancy</code></dt><dd><p>tenancy, indicating home ownership; yes = 1, no = 0</p>
</dd>
<dt><code>suppl.income</code></dt><dd><p>supplemental income, indicating whether
some form of supplemental security income is received; yes = 1, no = 0</p>
</dd>
<dt><code>income</code></dt><dd><p>monthly income (in US dollars)</p>
</dd>
</dl>



<h3>Source</h3>

<p>Data description and first analysis: Stefanski et al.(1986) who
indicate Rizek(1978) as original source of the larger study.
</p>
<p>Electronic version from CRAN package <span class="pkg">catdata</span>.

</p>


<h3>References</h3>

<p>Rizek, R. L. (1978)
The 1977-78 Nationwide Food Consumption Survey.
<em>Family Econ. Rev.</em>, Fall, 3&ndash;7.
</p>

<p>Stefanski, L. A., Carroll, R. J. and Ruppert, D. (1986)
Optimally bounded score functions for generalized linear models with
applications to logistic regression.
<em>Biometrika</em> <b>73</b>, 413&ndash;424.
</p>
<p>Künsch, H. R., Stefanski, L. A., Carroll, R. J. (1989)
Conditionally unbiased bounded-influence estimation in general
regression models, with applications to generalized linear models.
<em>J. American Statistical Association</em> <b>84</b>, 460&ndash;466.
</p>


<h3>Examples</h3>

<pre>
data(foodstamp)

(T123 &lt;- xtabs(~ participation+ tenancy+ suppl.income, data=foodstamp))
summary(T123) ## ==&gt; the binary var's are clearly not independent

foodSt &lt;- within(foodstamp, {
   logInc &lt;- log(1 + income)
   rm(income)
})

m1 &lt;- glm(participation ~ ., family=binomial, data=foodSt)
summary(m1)
rm1 &lt;- glmrob(participation ~ ., family=binomial, data=foodSt)
summary(rm1)
## Now use robust weights.on.x :
rm2 &lt;- glmrob(participation ~ ., family=binomial, data=foodSt,
              weights.on.x = "robCov")
summary(rm2)## aha, now the weights are different:
which( weights(rm2, type="robust") &lt; 0.5)
</pre>


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